Diagnostic systems are commonly used to test electronics, avionics systems, mechanical systems and to diagnose potential operational issues pertaining thereto. For example, certain diagnostic systems include test programs that are designed to identify potential faults in electronic systems. Such test programs may be used to run a series of independent tests on a particular electronic system to determine if there are any faults in the electronic system and, if so, to further determine the nature of such faults.
To further facilitate fault determination, certain diagnostic systems include a diagnostic reasoner based one or more artificial intelligence techniques. A diagnostic reasoner is generally designed to interactively function with other test software and/or a human maintainer. Diagnostic reasoners can provide improved testing and monitoring of electronics, avionics systems and mechanical systems.
Many diagnostic reasoner approaches are architected such that the reasoner system consists of executable reasoner software plus one or more diagnostic reasoner models (data) to hold parameters, correlations, relationships, rules and other data. The motivation for the split between reasoner software and model data may be to facilitate updates, and/or to allow a single reasoner software executable operate on a variety of systems under test through the use of distinct models for each system.
The accuracy and performance of the diagnostic reasoner is dependent on the fidelity of the diagnostic reasoner models it uses. Accordingly, there is a need for a method of learning, optimizing performance, and/or updating of a diagnostic reasoner model. The present invention addresses one or more of these needs.